This statistic shows the most affordable metro areas in the Unites States in 2017, by share of income spent on living expenses. In 2017, Omaha was the second most affordable metro area because ***** percent of the median blending annual household income was spent on the average cost of owning or renting a home as well the average cost of utilities and taxes.

Of the most populous cities in the U.S., San Jose, California had the highest annual income requirement at ******* U.S. dollars annually for homeowners to have an affordable and comfortable life in 2024. This can be compared to Houston, Texas, where homeowners needed an annual income of ****** U.S. dollars in 2024.

There is more to housing affordability than the rent or mortgage you pay. Transportation costs are the second-biggest budget item for most families, but it can be difficult for people to fully factor transportation costs into decisions about where to live and work. The Location Affordability Index (LAI) is a user-friendly source of standardized data at the neighborhood (census tract) level on combined housing and transportation costs to help consumers, policymakers, and developers make more informed decisions about where to live, work, and invest. Compare eight household profiles (see table below) —which vary by household income, size, and number of commuters—and see the impact of the built environment on affordability in a given location while holding household demographics constant.*$11,880 for a single person household in 2016 according to US Dept. of Health and Human Services: https://aspe.hhs.gov/computations-2016-poverty-guidelinesThis layer is symbolized by the percentage of housing and transportation costs as a percentage of income for the Median-Income Family profile, but the costs as a percentage of income for all household profiles are listed in the pop-up:Also available is a gallery of 8 web maps (one for each household profile) all symbolized the same way for easy comparison: Median-Income Family, Very Low-Income Individual, Working Individual, Single Professional, Retired Couple, Single-Parent Family, Moderate-Income Family, and Dual-Professional Family.An accompanying story map provides side-by-side comparisons and additional context.--Variables used in HUD's calculations include 24 measures such as people per household, average number of rooms per housing unit, monthly housing costs (mortgage/rent as well as utility and maintenance expenses), average number of cars per household, median commute distance, vehicle miles traveled per year, percent of trips taken on transit, street connectivity and walkability (measured by block density), and many more.To learn more about the Location Affordability Index (v.3) visit: https://www.hudexchange.info/programs/location-affordability-index/. There you will find some background and an FAQ page, which includes the question:"Manhattan, San Francisco, and downtown Boston are some of the most expensive places to live in the country, yet the LAI shows them as affordable for the typical regional household. Why?" These areas have some of the lowest transportation costs in the country, which helps offset the high cost of housing. The area median income (AMI) in these regions is also high, so when costs are shown as a percent of income for the typical regional household these neighborhoods appear affordable; however, they are generally unaffordable to households earning less than the AMI.Date of Coverage: 2012-2016 Date Released: March 2019Date Downloaded from HUD Open Data: 4/18/19Further Documentation:LAI Version 3 Data and MethodologyLAI Version 3 Technical Documentation_**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**

Title: Location Affordability Index - NMCDC Copy

Summary: This layer contains the Location Affordability Index from U.S. Dept. of Housing and Urban Development (HUD) - standardized household, housing, and transportation cost estimates by census tract for 8 household profiles.

Notes: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas.

Prepared by: dianaclavery_uo, copied by EMcRae_NMCDC

Source: This map is copied from source map: https://nmcdc.maps.arcgis.com/home/item.html?id=de341c1338c5447da400c4e8c51ae1f6, created by dianaclavery_uo, and identified in Living Atlas. Check the source documentation or other details above for more information about data sources.

Feature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=447a461f048845979f30a2478b9e65bb

UID: 73

Data Requested: Family income spent on basic need

Method of Acquisition: Search for Location Affordability Index in the Living Atlas. Make a copy of most recent map available. To update this map, copy the most recent map available. In a new tab, open the AGOL Assistant Portal tool and use the functions in the portal to copy the new maps JSON, and paste it over the old map (this map with item id

Date Acquired: Map copied on May 10, 2022

Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 6

Tags: PENDING

VITAL SIGNS INDICATOR Poverty (EQ5)

FULL MEASURE NAME The share of the population living in households that earn less than 200 percent of the federal poverty limit

LAST UPDATED December 2018

DESCRIPTION Poverty refers to the share of the population living in households that earn less than 200 percent of the federal poverty limit, which varies based on the number of individuals in a given household. It reflects the number of individuals who are economically struggling due to low household income levels.

DATA SOURCE U.S Census Bureau: Decennial Census http://www.nhgis.org (1980-1990) http://factfinder2.census.gov (2000)

U.S. Census Bureau: American Community Survey Form C17002 (2006-2017) http://api.census.gov

METHODOLOGY NOTES (across all datasets for this indicator) The U.S. Census Bureau defines a national poverty level (or household income) that varies by household size, number of children in a household, and age of householder. The national poverty level does not vary geographically even though cost of living is different across the United States. For the Bay Area, where cost of living is high and incomes are correspondingly high, an appropriate poverty level is 200% of poverty or twice the national poverty level, consistent with what was used for past equity work at MTC and ABAG. For comparison, however, both the national and 200% poverty levels are presented.

For Vital Signs, the poverty rate is defined as the number of people (including children) living below twice the poverty level divided by the number of people for whom poverty status is determined. Poverty rates do not include unrelated individuals below 15 years old or people who live in the following: institutionalized group quarters, college dormitories, military barracks, and situations without conventional housing. The household income definitions for poverty change each year to reflect inflation. The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). For the national poverty level definitions by year, see: https://www.census.gov/hhes/www/poverty/data/threshld/index.html For an explanation on how the Census Bureau measures poverty, see: https://www.census.gov/hhes/www/poverty/about/overview/measure.html

For the American Community Survey datasets, 1-year data was used for region, county, and metro areas whereas 5-year rolling average data was used for city and census tract.

To be consistent across metropolitan areas, the poverty definition for non-Bay Area metros is twice the national poverty level. Data were not adjusted for varying income and cost of living levels across the metropolitan areas.

U.S. Software Developer Salaries

Analyzing Regional Variations

By [source]

About this dataset

This dataset provides an extensive look into the financial health of software developers in major cities and metropolitan areas around the United States. We explore disparities between states and cities in terms of mean software developer salaries, median home prices, cost of living avgs, rent avgs, cost of living plus rent avgs and local purchasing power averages. Through this data set we can gain insights on how to better understand which areas are more financially viable than others when seeking employment within the software development field. Our data allow us to uncover patterns among certain geographic locations in order to identify other compelling financial opportunities that software developers may benefit from

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How to use the dataset

This dataset contains valuable information about software developer salaries across states and cities in the United States. It is important for recruiters and professionals alike to understand what kind of compensation software developers are likely to receive, as it may be beneficial when considering job opportunities or applying for a promotion. This guide will provide an overview of what you can learn from this dataset.

The data is organized by metropolitan areas, which encompass multiple cities within the same geographical region (e.g., “New York-Northern New Jersey” covers both New York City and Newark). From there, each metro can be broken down further into a number of different factors that may affect software developer salaries in the area:

• Mean Software Developer Salary (adjusted): The average salary of software developers in that particular metro area after accounting for cost of living differences within the region.
• Mean Software Developer Salary (unadjusted): The average salary of software developers in that particular metro area before adjusting for cost-of-living discrepancies between locales.
• Number of Software Developer Jobs: This column lists how many total jobs are available to software developers in this particular metropolitan area.
• Median Home Price: A metric which shows median value of all homes currently on the market within this partcular city or state. It helps gauge how expensive housing costs might be to potential residents who already have an idea about their income/salary range expectations when considering a move/relocation into another location or potentially looking at mortgage/rental options etc.. 5) Cost Of Living Avg: A metric designed to measure affordability using local prices paid on common consumer goods like food , transportation , health care , housing & other services etc.. Also prominent here along with rent avg ,cost od living plus rent avg helping compare relative cost structures between different locations while assessing potential remunerations & risk associated with them . 6)Local Purchasing Power Avg : A measure reflecting expected difference in discretionary spending ability among households regardless their income level upon relocation due to price discrepancies across locations allows individual assessment critical during job search particularly regarding relocation as well as comparison based decision making across prospective candidates during any hiring process . 7 ) Rent Avg : Average rental costs for homes / apartments dealbreakers even among prime job prospects particularly medium income earners.(basis family size & other constraints ) 8 ) Cost Of Living Plus Rent Avg : Used here as one sized fits perspective towards measuring overall cost structure including items

Research Ideas

• Comparing salaries of software developers in different cities to determine which city provides the best compensation package.
• Estimating the cost of relocating to a new city by looking at average costs such as rent and cost of living.
• Predicting job growth for software developers by analyzing factors like local purchasing power, median home price and number of jobs available

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking perm...

South Korea's capital Seoul had the highest cost of living among megacities in the Asia-Pacific region in 2024, with an index score of ****. Japan's capital Tokyo followed with a cost of living index score of ****. AffordabilityIn terms of housing affordability, Chinese megacity Shanghai had the highest rent index score in 2024. Affordability has become an issue in certain megacities across the Asia-Pacific region, with accommodation proving expensive. Next to Shanghai, Japanese capital Tokyo and South Korean capital Seoul boast some of the highest rent indices in the region. Increased opportunities in megacitiesAs the biggest region in the world, it is not surprising that the Asia-Pacific region is home to 28 megacities as of January 2024, with expectations that this number will dramatically increase by 2030. The growing number of megacities in the Asia-Pacific region can be attributed to raised levels of employment and living conditions. Cities such as Tokyo, Shanghai, and Beijing have become economic and industrial hubs. Subsequently, these cities have forged a reputation as being the in-trend places to live among the younger generations. This reputation has also pushed them to become enticing to tourists, with Tokyo displaying increased numbers of tourists throughout recent years, which in turn has created more job opportunities for inhabitants. As well as Tokyo, Shanghai has benefitted from the increased tourism, and has demonstrated an increasing population. A big factor in this population increase could be due to the migration of citizens to the city, seeking better employment possibilities.

VITAL SIGNS INDICATOR Poverty (EQ5)

FULL MEASURE NAME The share of the population living in households that earn less than 200 percent of the federal poverty limit

LAST UPDATED December 2018

DESCRIPTION Poverty refers to the share of the population living in households that earn less than 200 percent of the federal poverty limit, which varies based on the number of individuals in a given household. It reflects the number of individuals who are economically struggling due to low household income levels.

DATA SOURCE U.S Census Bureau: Decennial Census http://www.nhgis.org (1980-1990) http://factfinder2.census.gov (2000)

U.S. Census Bureau: American Community Survey Form C17002 (2006-2017) http://api.census.gov

METHODOLOGY NOTES (across all datasets for this indicator) The U.S. Census Bureau defines a national poverty level (or household income) that varies by household size, number of children in a household, and age of householder. The national poverty level does not vary geographically even though cost of living is different across the United States. For the Bay Area, where cost of living is high and incomes are correspondingly high, an appropriate poverty level is 200% of poverty or twice the national poverty level, consistent with what was used for past equity work at MTC and ABAG. For comparison, however, both the national and 200% poverty levels are presented.

For Vital Signs, the poverty rate is defined as the number of people (including children) living below twice the poverty level divided by the number of people for whom poverty status is determined. Poverty rates do not include unrelated individuals below 15 years old or people who live in the following: institutionalized group quarters, college dormitories, military barracks, and situations without conventional housing. The household income definitions for poverty change each year to reflect inflation. The official poverty definition uses money income before taxes and does not include capital gains or noncash benefits (such as public housing, Medicaid, and food stamps). For the national poverty level definitions by year, see: https://www.census.gov/hhes/www/poverty/data/threshld/index.html For an explanation on how the Census Bureau measures poverty, see: https://www.census.gov/hhes/www/poverty/about/overview/measure.html

For the American Community Survey datasets, 1-year data was used for region, county, and metro areas whereas 5-year rolling average data was used for city and census tract.

To be consistent across metropolitan areas, the poverty definition for non-Bay Area metros is twice the national poverty level. Data were not adjusted for varying income and cost of living levels across the metropolitan areas.

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for Loudoun County, VA (MWACL51107) from 2009 to 2023 about Loudoun County, VA; Washington; adjusted; VA; average; wages; real; and USA.

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for Alexandria city, VA (MWACL51510) from 2009 to 2023 about Alexandria City, VA; Washington; adjusted; VA; average; wages; real; and USA.

Graph and download economic data for Estimated Mean Real Household Wages Adjusted by Cost of Living for Fauquier County, VA (MWACL51061) from 2009 to 2023 about Fauquier County, VA; Washington; adjusted; VA; average; wages; real; and USA.

Population with Income per Capita below Living Cost: % of Total: CF: Moscow Region data was reported at 4.900 % in 2024. This records a decrease from the previous number of 5.500 % for 2023. Population with Income per Capita below Living Cost: % of Total: CF: Moscow Region data is updated yearly, averaging 9.850 % from Dec 1995 (Median) to 2024, with 30 observations. The data reached an all-time high of 35.200 % in 2000 and a record low of 4.900 % in 2024. Population with Income per Capita below Living Cost: % of Total: CF: Moscow Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA015: Population with Income per Capita below Living Cost.

Population with Income per Capita below Living Cost: % of Total: CF: Kursk Region data was reported at 6.300 % in 2024. This records a decrease from the previous number of 7.600 % for 2023. Population with Income per Capita below Living Cost: % of Total: CF: Kursk Region data is updated yearly, averaging 10.950 % from Dec 1995 (Median) to 2024, with 30 observations. The data reached an all-time high of 42.200 % in 2000 and a record low of 6.300 % in 2024. Population with Income per Capita below Living Cost: % of Total: CF: Kursk Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA015: Population with Income per Capita below Living Cost.

Montevideo, Uruguay's capital, leads Latin American cities with the highest apartment sale prices in 2025, averaging ***** U.S. dollars per square meter. This figure surpasses other major metropolitan areas like Mexico City and Buenos Aires, highlighting significant disparities in real estate markets across the region. The data underscores the varying economic conditions and housing demand in different Latin American urban centers. Regional housing market trends While Montevideo tops the list for apartment prices, other countries in Latin America have experienced notable changes in their housing markets. Chile, for instance, saw the most substantial increase in house prices since 2010, with its nominal house price index surpassing *** points in early 2025. However, when adjusted for inflation, Mexico showed the highest inflation-adjusted percentage increase in house prices, growing by nearly *** percent in the first quarter of 2025, contrasting with a global decline of one percent. Home financing in Mexico The methods of home financing vary across Latin America. A breakdown of homeownership by financing method in Mexico reveals that about two-thirds of owner-occupied housing units were financed through personal resources in 2022. Nevertheless, government-backed loans such as Infonavit (Mexico’s National Housing Fund Institute), Fovissste (Housing Fund of the Institute for Social Security and Services for State Workers), and Fonhapo (National Fund for Popular Housing), play an important role for homebuyers, with just over ** percent of home purchases relying on such finance. Bank credit, which offers mortgage loans with interest rates ranging between **** and ** percent, appeared as a less popular option.

Real household disposable income per person in the United Kingdom is expected to grow by 2.6 percent in 2024/25, with disposable income growth slowing from that point onwards. In 2022/23, disposable income fell by two percent, after falling by 0.1 percent in 2021/22, and 0.3 percent in 2020/21.

Geneva stands out as Europe's most expensive city for apartment purchases in early 2025, with prices reaching a staggering 15,720 euros per square meter. This Swiss city's real estate market dwarfs even high-cost locations like Zurich and London, highlighting the extreme disparities in housing affordability across the continent. The stark contrast between Geneva and more affordable cities like Nantes, France, where the price was 3,700 euros per square meter, underscores the complex factors influencing urban property markets in Europe. Rental market dynamics and affordability challenges While purchase prices vary widely, rental markets across Europe also show significant differences. London maintained its position as the continent's priciest city for apartment rentals in 2023, with the average monthly costs for a rental apartment amounting to 36.1 euros per square meter. This figure is double the rent in Lisbon, Portugal or Madrid, Spain, and substantially higher than in other major capitals like Paris and Berlin. The disparity in rental costs reflects broader economic trends, housing policies, and the intricate balance of supply and demand in urban centers. Economic factors influencing housing costs The European housing market is influenced by various economic factors, including inflation and energy costs. As of April 2025, the European Union's inflation rate stood at 2.4 percent, with significant variations among member states. Romania experienced the highest inflation at 4.9 percent, while France and Cyprus maintained lower rates. These economic pressures, coupled with rising energy costs, contribute to the overall cost of living and housing affordability across Europe. The volatility in electricity prices, particularly in countries like Italy where rates are projected to reach 153.83 euros per megawatt hour by February 2025, further impacts housing-related expenses for both homeowners and renters.

This dataset comprises information essential for predicting housing prices in Moscow and the Moscow Oblast region. Collected in November 2023, the data is current and pertinent for analysis. It includes various attributes crucial for predicting housing costs, such as location, size, amenities, and other relevant factors influencing property prices.

Description columns

Price: The price of the apartment in the specified currency. This is the primary target variable for prediction.

Apartment type: The type of apartment, such as studio, one-bedroom, two-bedroom, etc.

Metro station: The name of the nearest metro station to the apartment's location.

Minutes to metro: The time in minutes required to walk from the apartment to the nearest metro station.

Region: The region where the apartment is located (Moscow or Moscow Oblast).

Number of rooms: The total number of rooms in the apartment, including bedrooms, living rooms, etc.

Area: The total area of the apartment in square meters.

Living area: The living area of the apartment in square meters, i.e., the area usable for living.

Kitchen area: The area of the kitchen in square meters.

Floor: The floor on which the apartment is located.

Number of floors: The total number of floors in the building where the apartment is located.

Renovation: The level of renovation of the apartment, such as "no renovation", "cosmetic renovation", "euro renovation", etc.

Task for dataset users:

1. Using the housing price data for Moscow and the Moscow Oblast region, build a machine learning model to predict apartment prices. Your model should utilize various apartment attributes such as apartment type, proximity to metro stations, size, floor level, and renovation level to predict property prices. After constructing the model, analyze its performance and determine which factors have the most significant impact on housing prices in the Moscow and Moscow Oblast region.

A few questions to analyze this data set:

1. What are the most common types of apartments in the region?
2. Is there a relationship between housing prices and proximity to metro stations?
3. How does the level of renovation affect the price of an apartment?
4. Is there a price difference in housing between Moscow and the Moscow Oblast region?
5. Are there any preferences regarding floor levels?
6. Which factors have the greatest impact on housing prices?

Population with Income per Capita below Living Cost: % of Total: CF: Bryansk Region data was reported at 9.100 % in 2024. This records a decrease from the previous number of 11.400 % for 2023. Population with Income per Capita below Living Cost: % of Total: CF: Bryansk Region data is updated yearly, averaging 14.500 % from Dec 1995 (Median) to 2024, with 30 observations. The data reached an all-time high of 42.300 % in 2000 and a record low of 9.100 % in 2024. Population with Income per Capita below Living Cost: % of Total: CF: Bryansk Region data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA015: Population with Income per Capita below Living Cost.

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The "MoscowHomes" dataset presents a comprehensive collection of data crucial for predicting housing prices within Moscow and the Moscow Oblast region. Compiled in November 2023, the dataset remains current and relevant for insightful analysis. It encompasses a range of attributes essential for forecasting housing costs, including location, size, amenities, and other pertinent factors influencing property prices.

One of the key columns in the dataset is "Price," representing the apartment's price in the specified currency, serving as the primary target variable for prediction. Additionally, attributes such as "Apartment type" denote the classification of the apartment, ranging from studios to multi-bedroom units. "Metro station" identifies the nearest metro station to the apartment's location, while "Minutes to metro" quantifies the walking time required to reach the station. The "Region" column distinguishes whether the property is situated in Moscow or the Moscow Oblast region.

Further attributes provide insight into the physical characteristics of the apartments. "Number of rooms" indicates the total room count, "Area" specifies the apartment's total area in square meters, and "Living area" denotes the usable living space. "Kitchen area" quantifies the kitchen's size, while "Floor" and "Number of floors" offer information on the apartment's vertical position within the building. The "Renovation" column describes the level of refurbishment, ranging from "no renovation" to "euro renovation."

The dataset's primary task challenges users to develop a machine learning model to predict apartment prices based on the provided attributes. Through leveraging apartment type, metro station proximity, size, floor level, and renovation status, users can construct a robust predictive model. Following model construction, analysis should focus on performance evaluation and the identification of influential factors shaping housing prices in the region.

Several questions arise for analysis within this dataset. Exploring common apartment types, investigating the correlation between housing prices and metro station proximity, and assessing the impact of renovation levels on apartment prices are key inquiries. Additionally, understanding price disparities between Moscow and the Moscow Oblast region, discerning preferences for floor levels, and determining the most significant factors influencing housing prices are integral aspects of the analysis.

In summary, the "MoscowHomes" dataset offers a rich resource for exploring and understanding the dynamics of housing prices in Moscow and the surrounding region. Through rigorous analysis and model development, stakeholders can gain valuable insights into the factors driving property prices and make informed decisions within the real estate market.

Msa Safety reported $382.48M in Operating Expenses for its fiscal quarter ending in June of 2025. Data for MSA Safety | MSA - Operating Expenses including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Key findings in the Struggling to Get By report show that one in three California households (31%) do not have sufficient income to meet their basic costs of living. This is nearly three times the number officially considered poor according to the Federal Poverty Level.Families with inadequate incomes are found throughout California, but are most concentrated in the northern coastal region, the Central Valley, and in the southern metropolitan areas.The costs for the same family composition in different geographic regions of California also vary widely. In expensive regions such as the San Francisco Bay Region and the Southern California coastal region, the Real Cost Budget, a monthly budget calculation of what is needed to meet basic needs, can range from 32% to 48% more (depending on family type) than in less expensive counties such as Kern, Tulare, and Kings counties. Nevertheless, incomes in the higher cost regions are also higher, relatively and absolutely, so that the proportions below the Real Cost Measure are generally lower in high-cost than low-cost regions.